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spotify <- read.csv("Popular_Spotify_Songs.csv")
head(spotify)

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spotify <- read.csv("Popular_Spotify_Songs.csv")
head(spotify)

#spotify[cols_to_convert] <- lapply(spotify[cols_to_convert], function(x) as.numeric(as.character(x)))

#spotify$streams <- as.numeric(spotify$streams)
spotify$in_deezer_playlists = as.numeric(spotify$in_deezer_playlists)

G2;H2;Warningh: NAs introduced by coerciong

spotify$in_shazam_charts = as.numeric(spotify$in_shazam_charts)

G2;H2;Warningh: NAs introduced by coerciong

str(spotify[, 3:14])

'data.frame':   953 obs. of  12 variables:
 $ artist_count        : int  2 1 1 1 1 2 2 1 1 2 ...
 $ released_year       : int  2023 2023 2023 2019 2023 2023 2023 2023 2023 2023 ...
 $ released_month      : int  7 3 6 8 5 6 3 7 5 3 ...
 $ released_day        : int  14 23 30 23 18 1 16 7 15 17 ...
 $ in_spotify_playlists: int  553 1474 1397 7858 3133 2186 3090 714 1096 2953 ...
 $ in_spotify_charts   : int  147 48 113 100 50 91 50 43 83 44 ...
 $ streams             : num  1.41e+08 1.34e+08 1.40e+08 8.01e+08 3.03e+08 ...
 $ in_apple_playlists  : int  43 48 94 116 84 67 34 25 60 49 ...
 $ in_apple_charts     : int  263 126 207 207 133 213 222 89 210 110 ...
 $ in_deezer_playlists : num  45 58 91 125 87 88 43 30 48 66 ...
 $ in_deezer_charts    : int  10 14 14 12 15 17 13 13 11 13 ...
 $ in_shazam_charts    : num  826 382 949 548 425 946 418 194 953 339 ...

pairs(spotify[, 3:14], main = "Linear Relationships Between Metrics")

summary(spotify)

  track_name        artist.s._name      artist_count   released_year  released_month    released_day   in_spotify_playlists in_spotify_charts
 Length:953         Length:953         Min.   :1.000   Min.   :1930   Min.   : 1.000   Min.   : 1.00   Min.   :   31        Min.   :  0.00   
 Class :character   Class :character   1st Qu.:1.000   1st Qu.:2020   1st Qu.: 3.000   1st Qu.: 6.00   1st Qu.:  875        1st Qu.:  0.00   
 Mode  :character   Mode  :character   Median :1.000   Median :2022   Median : 6.000   Median :13.00   Median : 2224        Median :  3.00   
                                       Mean   :1.556   Mean   :2018   Mean   : 6.034   Mean   :13.93   Mean   : 5200        Mean   : 12.01   
                                       3rd Qu.:2.000   3rd Qu.:2022   3rd Qu.: 9.000   3rd Qu.:22.00   3rd Qu.: 5542        3rd Qu.: 16.00   
                                       Max.   :8.000   Max.   :2023   Max.   :12.000   Max.   :31.00   Max.   :52898        Max.   :147.00   
                                                                                                                                             
    streams          in_apple_playlists in_apple_charts  in_deezer_playlists in_deezer_charts in_shazam_charts      bpm            key           
 Min.   :2.762e+03   Min.   :  0.00     Min.   :  0.00   Min.   :  0.0       Min.   : 0.000   Min.   :  0.00   Min.   : 65.0   Length:953        
 1st Qu.:1.416e+08   1st Qu.: 13.00     1st Qu.:  7.00   1st Qu.: 12.0       1st Qu.: 0.000   1st Qu.:  0.00   1st Qu.:100.0   Class :character  
 Median :2.905e+08   Median : 34.00     Median : 38.00   Median : 36.5       Median : 0.000   Median :  2.00   Median :121.0   Mode  :character  
 Mean   :5.141e+08   Mean   : 67.81     Mean   : 51.91   Mean   :109.7       Mean   : 2.666   Mean   : 51.18   Mean   :122.5                     
 3rd Qu.:6.739e+08   3rd Qu.: 88.00     3rd Qu.: 87.00   3rd Qu.:110.0       3rd Qu.: 2.000   3rd Qu.: 36.00   3rd Qu.:140.0                     
 Max.   :3.704e+09   Max.   :672.00     Max.   :275.00   Max.   :974.0       Max.   :58.000   Max.   :953.00   Max.   :206.0                     
 NA's   :1                                               NA's   :79                           NA's   :57                                         
     mode           danceability_.    valence_.        energy_.     acousticness_.  instrumentalness_.   liveness_.    speechiness_.  
 Length:953         Min.   :23.00   Min.   : 4.00   Min.   : 9.00   Min.   : 0.00   Min.   : 0.000     Min.   : 3.00   Min.   : 2.00  
 Class :character   1st Qu.:57.00   1st Qu.:32.00   1st Qu.:53.00   1st Qu.: 6.00   1st Qu.: 0.000     1st Qu.:10.00   1st Qu.: 4.00  
 Mode  :character   Median :69.00   Median :51.00   Median :66.00   Median :18.00   Median : 0.000     Median :12.00   Median : 6.00  
                    Mean   :66.97   Mean   :51.43   Mean   :64.28   Mean   :27.06   Mean   : 1.581     Mean   :18.21   Mean   :10.13  
                    3rd Qu.:78.00   3rd Qu.:70.00   3rd Qu.:77.00   3rd Qu.:43.00   3rd Qu.: 0.000     3rd Qu.:24.00   3rd Qu.:11.00  
                    Max.   :96.00   Max.   :97.00   Max.   :97.00   Max.   :97.00   Max.   :91.000     Max.   :97.00   Max.   :64.00  
                                                                                                                                      

colSums(is.na(spotify))

          track_name       artist.s._name         artist_count        released_year       released_month         released_day in_spotify_playlists 
                   0                    0                    0                    0                    0                    0                    0 
   in_spotify_charts              streams   in_apple_playlists      in_apple_charts  in_deezer_playlists     in_deezer_charts     in_shazam_charts 
                   0                    1                    0                    0                   79                    0                   57 
                 bpm                  key                 mode       danceability_.            valence_.             energy_.       acousticness_. 
                   0                    0                    0                    0                    0                    0                    0 
  instrumentalness_.           liveness_.        speechiness_. 
                   0                    0                    0 

dim(spotify)

[1] 953  24

library(ggplot2)

ggplot(spotify, aes(x = released_year)) +
  geom_histogram(binwidth = 1, fill = "skyblue", color = "white") +
  labs(title = "Distribution of Streams", x = names(spotify$released_year))

plot(density(spotify$released_year, na.rm = TRUE), main = "Density Plot of Released Year", xlab = "Released Year", col = "blue", lwd = 2)

View(spotify)

# Basic scatter plot with color based on 'mode'
ggplot(spotify, aes(x = streams, y = in_spotify_playlists, color = mode)) +
  geom_point() +
  labs(title = "Streams vs Playlist Metrics by Mode",
       x = "Streams",
       y = "Number in Spotify Playlists") +
  theme_minimal()

library(shiny)
library(ggplot2)

# UI
ui <- fluidPage(
  titlePanel("Streams vs Spotify Playlists by Mode"),
  sidebarLayout(
    sidebarPanel(
      checkboxGroupInput("mode_select", "Select Mode(s):",
                         choices = unique(spotify$mode),
                         selected = unique(spotify$mode))
    ),
    mainPanel(
      plotOutput("scatterPlot")
    )
  )
)

# Server
server <- function(input, output) {
  output$scatterPlot <- renderPlot({
    filtered_data <- spotify[spotify$mode %in% input$mode_select, ]

    ggplot(filtered_data, aes(x = streams, y = in_spotify_playlists, color = mode)) +
      geom_point() +
      labs(
        title = "Streams vs Playlist Metrics by Mode",
        x = "Streams",
        y = "Number in Spotify Playlists"
      ) +
      theme_minimal()
  })
}

# Run the app
shinyApp(ui = ui, server = server)

G3;
Listening on http://127.0.0.1:7921
g

library(dplyr)
# Create a combined label of Song + Artist
spotify <- spotify %>%
  mutate(song.artist = paste(track_name, "-", artist.s._name))

yearly_top_song <- spotify %>%
  group_by(released_year) %>%
  slice_max(order_by = streams, n = 1, with_ties = TRUE) %>%
  ungroup()

# Step 1: Get the top 10 songs by total streams
top10_yearly <- yearly_top_song %>% 
  arrange(desc(streams)) %>% 
  slice(1:10)

top10_yearly

# Convert song_artist to factor with levels ordered by Streams
top10_yearly <- top10_yearly %>%
  arrange(desc(streams)) %>%
  mutate(song.artist = factor(song.artist, levels = unique(song.artist)))
top10_yearly

ggplot(top10_yearly, aes(x = released_year, y = streams, fill = factor(song.artist))) +
  geom_bar(stat = "identity") +
  labs(title = "Top Streamed Songs per Year",
       x = "Track (Song - Artist)",
       y = "Number of Streams",
       fill = "Year") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

ggplot(top10_yearly, aes(x = factor(released_year), y = streams, fill = song.artist)) +
  geom_bar(stat = "identity") +
  labs(title = "Top 10 Streamed Songs By Year",
       x = "Year",
       y = "Number of Streams",
       fill = "Song-Artist") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

yearly_top_songs <- spotify %>%
  group_by(released_year) %>%
  slice_max(order_by = streams, n = 5, with_ties = TRUE) %>%
  ungroup()

View(yearly_top_songs)

# Filter for 2023 top 5 songs from your previously filtered data
top_2023 <- yearly_top_songs %>%
  filter(released_year == 2023)

# Create the bar chart
ggplot(top_2023, aes(x = reorder(track_name, -streams), y = streams, fill = song.artist)) +
  geom_bar(stat = "identity") +
  labs(title = "Top 5 Streamed Songs in 2023",
       x = "Song",
       y = "Number of Streams",
       fill = "Song & Artist") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

# Check how many rows are in the dataset for 2022
dim(yearly_top_songs[yearly_top_songs$released_year == 2022, ])

[1]  5 25

# Check for NA or invalid values in 2022
summary(yearly_top_songs[yearly_top_songs$released_year == 2022, ])

  track_name        artist.s._name      artist_count released_year  released_month  released_day  in_spotify_playlists in_spotify_charts
 Length:5           Length:5           Min.   :1.0   Min.   :2022   Min.   :3.0    Min.   : 6.0   Min.   : 8506        Min.   : 42.0    
 Class :character   Class :character   1st Qu.:1.0   1st Qu.:2022   1st Qu.:5.0    1st Qu.: 6.0   1st Qu.: 8576        1st Qu.: 42.0    
 Mode  :character   Mode  :character   Median :2.0   Median :2022   Median :5.0    Median : 6.0   Median : 8870        Median : 43.0    
                                       Mean   :1.6   Mean   :2022   Mean   :5.8    Mean   :14.2   Mean   :11713        Mean   : 60.4    
                                       3rd Qu.:2.0   3rd Qu.:2022   3rd Qu.:7.0    3rd Qu.:22.0   3rd Qu.: 9037        3rd Qu.: 45.0    
                                       Max.   :2.0   Max.   :2022   Max.   :9.0    Max.   :31.0   Max.   :23575        Max.   :130.0    
                                                                                                                                        
    streams          in_apple_playlists in_apple_charts in_deezer_playlists in_deezer_charts in_shazam_charts      bpm            key           
 Min.   :1.231e+09   Min.   : 94.0      Min.   : 65.0   Min.   :139.0       Min.   :14.0     Min.   : 49.0    Min.   : 92.0   Length:5          
 1st Qu.:1.264e+09   1st Qu.:104.0      1st Qu.:108.0   1st Qu.:141.0       1st Qu.:14.0     1st Qu.:127.8    1st Qu.:107.0   Class :character  
 Median :1.357e+09   Median :124.0      Median :120.0   Median :164.0       Median :26.0     Median :160.0    Median :128.0   Mode  :character  
 Mean   :1.561e+09   Mean   :188.2      Mean   :124.8   Mean   :327.6       Mean   :25.2     Mean   :136.2    Mean   :126.4                     
 3rd Qu.:1.441e+09   3rd Qu.:216.0      3rd Qu.:133.0   3rd Qu.:331.0       3rd Qu.:26.0     3rd Qu.:168.5    3rd Qu.:131.0                     
 Max.   :2.513e+09   Max.   :403.0      Max.   :198.0   Max.   :863.0       Max.   :46.0     Max.   :176.0    Max.   :174.0                     
                                                                                             NA's   :1                                          
     mode           danceability_.   valence_.       energy_.    acousticness_. instrumentalness_.   liveness_.   speechiness_.  song.artist       
 Length:5           Min.   :52.0   Min.   :19.0   Min.   :47.0   Min.   : 1     Min.   :0.0        Min.   : 9.0   Min.   : 4.0   Length:5          
 Class :character   1st Qu.:62.0   1st Qu.:24.0   1st Qu.:71.0   1st Qu.: 1     1st Qu.:0.0        1st Qu.:13.0   1st Qu.: 6.0   Class :character  
 Mode  :character   Median :65.0   Median :43.0   Median :72.0   Median : 9     Median :0.0        Median :23.0   Median : 8.0   Mode  :character  
                    Mean   :68.2   Mean   :41.4   Mean   :68.2   Mean   :11     Mean   :0.6        Mean   :20.6   Mean   :10.4                     
                    3rd Qu.:71.0   3rd Qu.:55.0   3rd Qu.:73.0   3rd Qu.:10     3rd Qu.:0.0        3rd Qu.:27.0   3rd Qu.: 9.0                     
                    Max.   :91.0   Max.   :66.0   Max.   :78.0   Max.   :34     Max.   :3.0        Max.   :31.0   Max.   :25.0                     
                                                                                                                                                   

# Alternatively, print it to inspect
print(yearly_top_songs[yearly_top_songs$released_year == 2022, ])

# Look at 2022 data closely
spotify %>%
  filter(released_year == 2022) %>%
  select(track_name, artist.s._name, streams) %>%
  glimpse()

Rows: 402
Columns: 3
$ track_name     <chr> "As It Was", "Kill Bill", "Calm Down (with Selena Gomez)", "Creepin'", "Anti-Hero", "I'm Good (Blue)", "I Ain't Worried", "La Ba…
$ artist.s._name <chr> "Harry Styles", "SZA", "R��ma, Selena G", "The Weeknd, 21 Savage, Metro Boomin", "Taylor Swift", "Bebe Rexha, David Guetta", "On…
$ streams        <dbl> 2513188493, 1163093654, 899183384, 843957510, 999748277, 1109433169, 1085685420, 1214083358, 720434240, 674072710, 404562836, 37…

# Count unique songs to see if there's a tie issue
yearly_top_songs %>%
  filter(released_year == 2022) %>%
  count(track_name)

# Check for NAs in streams or grouping variables
yearly_top_songs %>%
  filter(released_year == 2022) %>%
  summarise(
    missing_streams = sum(is.na(streams)),
    missing_track = sum(is.na(track_name)),
    missing_artist = sum(is.na(song.artist)))

# Shiny app to view top streamed songs by year with a toggle
library(shiny)
library(dplyr)
library(ggplot2)

# UI
ui <- fluidPage(
  titlePanel("Top Streamed Songs by Year"),
  sidebarLayout(
    sidebarPanel(
      selectInput("year", "Select Year:", choices = sort(unique(yearly_top_songs$released_year)))
    ),
    mainPanel(
      plotOutput("topSongsPlot")
    )
  )
)

# Server
server <- function(input, output, session) {
  
  output$topSongsPlot <- renderPlot({
    selected_year_data <- yearly_top_songs %>% 
      filter(released_year == input$year)

    # Ensure no invalid characters or encoding issues in track names
    selected_year_data$track_name <- iconv(selected_year_data$track_name, from = "UTF-8", to = "UTF-8", sub = "*")

    ggplot(selected_year_data, aes(x = reorder(track_name, -streams), y = streams, fill = song.artist)) +
      geom_bar(stat = "identity") +
      labs(title = paste("Top 5 Streamed Songs in", input$year),
           x = "Song",
           y = "Number of Streams",
           fill = "Song & Artist") +
      theme_minimal() +
      theme(axis.text.x = element_text(angle = 45, hjust = 1))
  })
}

# Run app
shinyApp(ui = ui, server = server)

G3;
Listening on http://127.0.0.1:7921
g

# Select only numeric columns
numeric_cols <- spotify %>%
  select(where(is.numeric))

numeric_cols

# Calculate correlation of all numeric columns with 'streams'
correlations <- cor(numeric_cols, use = "complete.obs")
correlations

                     artist_count released_year released_month released_day in_spotify_playlists in_spotify_charts       streams in_apple_playlists
artist_count          1.000000000   0.061445644    0.009720347 -0.044766245        -0.0746868039      -0.002421656 -0.1090468634       -0.008712241
released_year         0.061445644   1.000000000    0.031372926  0.160042169        -0.3305741123       0.100988420 -0.1483509269       -0.155648773
released_month        0.009720347   0.031372926    1.000000000 -0.015820232        -0.0187633639      -0.031526077  0.0413240023        0.007380967
released_day         -0.044766245   0.160042169   -0.015820232  1.000000000        -0.0320967227       0.042203010  0.0410748362        0.028622345
in_spotify_playlists -0.074686804  -0.330574112   -0.018763364 -0.032096723         1.0000000000       0.173307807  0.7650951338        0.709922084
in_spotify_charts    -0.002421656   0.100988420   -0.031526077  0.042203010         0.1733078066       1.000000000  0.2454749140        0.213322335
streams              -0.109046863  -0.148350927    0.041324002  0.041074836         0.7650951338       0.245474914  1.0000000000        0.663657168
in_apple_playlists   -0.008712241  -0.155648773    0.007380967  0.028622345         0.7099220838       0.213322335  0.6636571679        1.000000000
in_apple_charts      -0.079655066   0.007650642   -0.010603129  0.009855360         0.2087053786       0.565321488  0.2508103705        0.322358302
in_deezer_playlists  -0.073406225  -0.265234545   -0.035433532 -0.041980554         0.7880546875       0.151785663  0.7185929567        0.645914528
in_deezer_charts      0.022218537   0.103287112   -0.001921194  0.063555630         0.1952907401       0.558419963  0.2594696320        0.409688235
in_shazam_charts     -0.031812269   0.054492378   -0.090799317  0.040728906         0.1111503800       0.594678886  0.0587456970        0.187401561
bpm                  -0.067047448  -0.041957657   -0.051936323 -0.048020996         0.0260085534       0.028010413  0.0327164251        0.044415122
danceability_.        0.209581804   0.192054100   -0.034978955  0.076211130        -0.1066197808       0.075249362 -0.0754316227        0.011504320
valence_.             0.120784211  -0.064812792   -0.118074232  0.071279071        -0.0552336199       0.056602171 -0.0584550791        0.053187299
energy_.              0.149966302   0.130105474   -0.081977712  0.064572106        -0.0494256700       0.104328458 -0.0496657926        0.074416649
acousticness_.       -0.101620287  -0.169751059    0.039266560 -0.010279631         0.0001543819      -0.078095007  0.0013286969       -0.088265650
instrumentalness_.   -0.052814944  -0.014754771    0.031122232  0.007126726         0.0121080272      -0.012565007 -0.0009670221       -0.045488723
liveness_.            0.041035230   0.007441171   -0.017825352  0.002619619        -0.0339739648      -0.039153639 -0.0387277529       -0.046255149
speechiness_.         0.117955768   0.126711891    0.030599526 -0.017347379        -0.0719087372      -0.086192083 -0.0907281501       -0.101941835
                     in_apple_charts in_deezer_playlists in_deezer_charts in_shazam_charts           bpm danceability_.    valence_.     energy_. acousticness_.
artist_count            -0.079655066        -0.073406225      0.022218537     -0.031812269 -0.0670474482    0.209581804  0.120784211  0.149966302  -0.1016202867
released_year            0.007650642        -0.265234545      0.103287112      0.054492378 -0.0419576571    0.192054100 -0.064812792  0.130105474  -0.1697510593
released_month          -0.010603129        -0.035433532     -0.001921194     -0.090799317 -0.0519363227   -0.034978955 -0.118074232 -0.081977712   0.0392665600
released_day             0.009855360        -0.041980554      0.063555630      0.040728906 -0.0480209963    0.076211130  0.071279071  0.064572106  -0.0102796313
in_spotify_playlists     0.208705379         0.788054688      0.195290740      0.111150380  0.0260085534   -0.106619781 -0.055233620 -0.049425670   0.0001543819
in_spotify_charts        0.565321488         0.151785663      0.558419963      0.594678886  0.0280104129    0.075249362  0.056602171  0.104328458  -0.0780950070
streams                  0.250810371         0.718592957      0.259469632      0.058745697  0.0327164251   -0.075431623 -0.058455079 -0.049665793   0.0013286969
in_apple_playlists       0.322358302         0.645914528      0.409688235      0.187401561  0.0444151222    0.011504320  0.053187299  0.074416649  -0.0882656498
in_apple_charts          1.000000000         0.198692411      0.356675982      0.443346418  0.0512089175   -0.003976097  0.061427394  0.153590558  -0.1050831002
in_deezer_playlists      0.198692411         1.000000000      0.218281108      0.135919298  0.0453831408   -0.104850821 -0.025849620 -0.028605485   0.0288379089
in_deezer_charts         0.356675982         0.218281108      1.000000000      0.374829138  0.0370517105    0.087187954  0.075155386  0.108571701  -0.0439176997
in_shazam_charts         0.443346418         0.135919298      0.374829138      1.000000000  0.0891578410   -0.010179394 -0.003080391  0.095095549  -0.0716735649
bpm                      0.051208918         0.045383141      0.037051710      0.089157841  1.0000000000   -0.140710959  0.050484657  0.003536259  -0.0020473755
danceability_.          -0.003976097        -0.104850821      0.087187954     -0.010179394 -0.1407109592    1.000000000  0.390335848  0.186243358  -0.2390078796
valence_.                0.061427394        -0.025849620      0.075155386     -0.003080391  0.0504846571    0.390335848  1.000000000  0.354253808  -0.0680708838
energy_.                 0.153590558        -0.028605485      0.108571701      0.095095549  0.0035362587    0.186243358  0.354253808  1.000000000  -0.5547718398
acousticness_.          -0.105083100         0.028837909     -0.043917700     -0.071673565 -0.0020473755   -0.239007880 -0.068070884 -0.554771840   1.0000000000
instrumentalness_.      -0.010658818         0.021617457     -0.002299823     -0.015732282 -0.0009552758   -0.098154216 -0.136058212 -0.032914831   0.0332206982
liveness_.              -0.001551996        -0.005142997      0.002914949     -0.045209630  0.0005645641   -0.093272303  0.016319569  0.120967010  -0.0406689579
speechiness_.           -0.157645853        -0.108361699     -0.073955127     -0.081685578  0.0247134810    0.173420342  0.036580343 -0.017125796  -0.0238770164
                     instrumentalness_.    liveness_. speechiness_.
artist_count              -0.0528149443  0.0410352297    0.11795577
released_year             -0.0147547713  0.0074411712    0.12671189
released_month             0.0311222324 -0.0178253521    0.03059953
released_day               0.0071267258  0.0026196188   -0.01734738
in_spotify_playlists       0.0121080272 -0.0339739648   -0.07190874
in_spotify_charts         -0.0125650073 -0.0391536392   -0.08619208
streams                   -0.0009670221 -0.0387277529   -0.09072815
in_apple_playlists        -0.0454887232 -0.0462551494   -0.10194183
in_apple_charts           -0.0106588177 -0.0015519961   -0.15764585
in_deezer_playlists        0.0216174566 -0.0051429975   -0.10836170
in_deezer_charts          -0.0022998235  0.0029149486   -0.07395513
in_shazam_charts          -0.0157322822 -0.0452096305   -0.08168558
bpm                       -0.0009552758  0.0005645641    0.02471348
danceability_.            -0.0981542162 -0.0932723026    0.17342034
valence_.                 -0.1360582123  0.0163195694    0.03658034
energy_.                  -0.0329148310  0.1209670100   -0.01712580
acousticness_.             0.0332206982 -0.0406689579   -0.02387702
instrumentalness_.         1.0000000000 -0.0488636800   -0.08664221
liveness_.                -0.0488636800  1.0000000000   -0.04518074
speechiness_.             -0.0866422067 -0.0451807367    1.00000000

# Extract just the correlations with 'streams'
cor_with_streams <- correlations["streams", ]
cor_with_streams

        artist_count        released_year       released_month         released_day in_spotify_playlists    in_spotify_charts              streams 
       -0.1090468634        -0.1483509269         0.0413240023         0.0410748362         0.7650951338         0.2454749140         1.0000000000 
  in_apple_playlists      in_apple_charts  in_deezer_playlists     in_deezer_charts     in_shazam_charts                  bpm       danceability_. 
        0.6636571679         0.2508103705         0.7185929567         0.2594696320         0.0587456970         0.0327164251        -0.0754316227 
           valence_.             energy_.       acousticness_.   instrumentalness_.           liveness_.        speechiness_. 
       -0.0584550791        -0.0496657926         0.0013286969        -0.0009670221        -0.0387277529        -0.0907281501 

# Sort and view
sort(cor_with_streams, decreasing = TRUE)

             streams in_spotify_playlists  in_deezer_playlists   in_apple_playlists     in_deezer_charts      in_apple_charts    in_spotify_charts 
        1.0000000000         0.7650951338         0.7185929567         0.6636571679         0.2594696320         0.2508103705         0.2454749140 
    in_shazam_charts       released_month         released_day                  bpm       acousticness_.   instrumentalness_.           liveness_. 
        0.0587456970         0.0413240023         0.0410748362         0.0327164251         0.0013286969        -0.0009670221        -0.0387277529 
            energy_.            valence_.       danceability_.        speechiness_.         artist_count        released_year 
       -0.0496657926        -0.0584550791        -0.0754316227        -0.0907281501        -0.1090468634        -0.1483509269 

model <- lm(streams ~ in_spotify_playlists + in_deezer_playlists + in_apple_playlists + danceability_. + energy_. + valence_., data = spotify)
summary(model)

Call:
lm(formula = streams ~ in_spotify_playlists + in_deezer_playlists + 
    in_apple_playlists + danceability_. + energy_. + valence_., 
    data = spotify)

Residuals:
       Min         1Q     Median         3Q        Max 
-1.068e+09 -1.246e+08 -3.334e+07  9.771e+07  1.301e+09 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)          182491364   45594165   4.003 6.80e-05 ***
in_spotify_playlists     42130       3557  11.844  < 2e-16 ***
in_deezer_playlists     566032      74514   7.596 7.90e-14 ***
in_apple_playlists     1507560     189228   7.967 5.11e-15 ***
danceability_.          223598     592024   0.378   0.7058    
energy_.               -717839     509189  -1.410   0.1590    
valence_.              -637129     379394  -1.679   0.0934 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 229500000 on 866 degrees of freedom
  (80 observations deleted due to missingness)
Multiple R-squared:  0.6886,    Adjusted R-squared:  0.6864 
F-statistic: 319.2 on 6 and 866 DF,  p-value: < 2.2e-16

# Refitting the model with only significant predictors
refined_model <- lm(streams ~ in_spotify_playlists + in_deezer_playlists + in_apple_playlists, data = spotify)

# Summary of the refined model
summary(refined_model)

Call:
lm(formula = streams ~ in_spotify_playlists + in_deezer_playlists + 
    in_apple_playlists, data = spotify)

Residuals:
       Min         1Q     Median         3Q        Max 
-1.082e+09 -1.173e+08 -3.519e+07  9.715e+07  1.311e+09 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)          119309688   10537578  11.322  < 2e-16 ***
in_spotify_playlists     43264       3526  12.271  < 2e-16 ***
in_deezer_playlists     565426      74598   7.580 8.88e-14 ***
in_apple_playlists     1427508     186160   7.668 4.67e-14 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.3e+08 on 869 degrees of freedom
  (80 observations deleted due to missingness)
Multiple R-squared:  0.686, Adjusted R-squared:  0.6849 
F-statistic: 632.9 on 3 and 869 DF,  p-value: < 2.2e-16

# Refit the model with complete cases only
data_complete <- spotify %>%
  select(streams, in_spotify_playlists, in_deezer_playlists, in_apple_playlists) %>%
  na.omit()

model <- lm(streams ~ in_spotify_playlists + in_deezer_playlists + in_apple_playlists, data = data_complete)

# Add predictions to the complete data

# Predict streams
predicted_streams <- predict(model, newdata = data_complete)

# Add predictions to the data frame
data_complete$predicted_streams <- predicted_streams
data_complete

# Plot actual vs predicted
ggplot(data_complete, aes(x = streams, y = predicted_streams)) +
  geom_point(alpha = 0.6, color = "steelblue") +
  geom_abline(intercept = 0, slope = 1, color = "red", linetype = "dashed") +
  labs(title = "Actual vs Predicted Streams",
       x = "Actual Streams",
       y = "Predicted Streams") +
  theme_minimal()

# Plot residuals
residuals <- model$residuals
View(data_complete)

ggplot(data_complete, aes(x = predicted_streams, y = residuals)) +
  geom_point(alpha = 0.6, color = "darkorange") +
  geom_hline(yintercept = 0, linetype = "dashed", color = "red") +
  labs(title = "Residual Plot",
       x = "Predicted Streams",
       y = "Residuals") +
  theme_minimal()

# Step 1: Load required libraries
library(caret)

# Step 2: Set seed for reproducibility
set.seed(123)

# Step 3: Define training control for 10-fold cross-validation
train_control <- trainControl(method = "cv", number = 10)

# Step 4: Define the model formula (same predictors as before)
model_formula <- streams ~ in_spotify_playlists + in_deezer_playlists + in_apple_playlists

# Step 5: Fit the linear regression model using caret::train()
cv_model <- train(
  model_formula,
  data = data_complete,
  method = "lm",
  trControl = train_control
)

# Step 6: Review cross-validation results
print(cv_model)

Linear Regression 

873 samples
  3 predictor

No pre-processing
Resampling: Cross-Validated (10 fold) 
Summary of sample sizes: 785, 786, 785, 786, 786, 787, ... 
Resampling results:

  RMSE       Rsquared   MAE      
  231655068  0.6849981  161789134

Tuning parameter 'intercept' was held constant at a value of TRUE

# Optional: Plot predictions vs. actuals again using cv_model$finalModel if desired

# Fit the final model on full data
final_model <- train(
  streams ~ in_spotify_playlists + in_deezer_playlists + in_apple_playlists,
  data = data_complete,
  method = "lm"
)

# View final coefficients
coef(final_model$finalModel)

         (Intercept) in_spotify_playlists  in_deezer_playlists   in_apple_playlists 
        119309687.75             43263.74            565425.67           1427508.47 

#Create a new data frame with predictor values
# Replace these numbers with your actual input values
new_input <- data.frame(
  in_spotify_playlists = 2500,
  in_deezer_playlists = 50,
  in_apple_playlists = 250
)

# 3. Predict streams based on new inputs
predicted_streams <- predict(cv_model, newdata = new_input)

# View prediction
predicted_streams

        1 
612617449 

# Load required packages
library(shiny)

# Define UI
ui <- fluidPage(
  titlePanel("Predict Song Streams"),
  sidebarLayout(
    sidebarPanel(
      numericInput("spotify", "Spotify Playlists:", value = 5000, min = 0),
      numericInput("deezer", "Deezer Playlists:", value = 1000, min = 0),
      numericInput("apple", "Apple Playlists:", value = 2000, min = 0),
      actionButton("predict", "Predict Streams")
    ),
    mainPanel(
      h3("Predicted Streams:"),
      verbatimTextOutput("prediction")
    )
  )
)

# Define server logic
server <- function(input, output) {

  # Reactive prediction
  observeEvent(input$predict, {
    new_input <- data.frame(
      in_spotify_playlists = input$spotify,
      in_deezer_playlists = input$deezer,
      in_apple_playlists = input$apple
    )

    predicted <- predict(cv_model, newdata = new_input)

    output$prediction <- renderText({
      format(round(predicted, 0), big.mark = ",")
    })
  })
}

# Run the application 
shinyApp(ui = ui, server = server)

G3;
Listening on http://127.0.0.1:7921
g

?cv_model

No documentation for ‘cv_model’ in specified packages and libraries:
you could try ‘??cv_model’

cv_model

Linear Regression 

873 samples
  3 predictor

No pre-processing
Resampling: Cross-Validated (10 fold) 
Summary of sample sizes: 785, 786, 785, 786, 786, 787, ... 
Resampling results:

  RMSE       Rsquared   MAE      
  231655068  0.6849981  161789134

Tuning parameter 'intercept' was held constant at a value of TRUE

# Assume `final_model` is already trained with lm()
colSums(is.na(data_complete))

             streams in_spotify_playlists  in_deezer_playlists   in_apple_playlists    predicted_streams 
                   0                    0                    0                    0                    0 

# Then you need to extract the final linear model from the `train` object
# before using it for prediction with confidence and prediction intervals

lm_model <- cv_model$finalModel

# Now you can safely use predict with interval = "confidence" and "prediction"
pred_conf <- predict(lm_model, newdata = data_complete, interval = "confidence")
pred_pred <- predict(lm_model, newdata = data_complete, interval = "prediction")

# Combine everything into a data frame
plot_data <- data_complete %>%
  mutate(
    predicted_streams = pred_conf[, "fit"],
    conf_low = pred_conf[, "lwr"],
    conf_high = pred_conf[, "upr"],
    pred_low = pred_pred[, "lwr"],
    pred_high = pred_pred[, "upr"]
  )

# Plot
ggplot(plot_data, aes(x = streams, y = predicted_streams)) +
  geom_point(alpha = 0.6, color = "darkblue") +
  geom_abline(intercept = 0, slope = 1, linetype = "dashed", color = "blue") +
  geom_ribbon(aes(ymin = conf_low, ymax = conf_high), fill = "lightblue", alpha = 0.3) +
  geom_ribbon(aes(ymin = pred_low, ymax = pred_high), fill = "orange", alpha = 0.2) +
  labs(
    title = "Predicted vs Actual Streams with Confidence and Prediction Intervals",
    x = "Actual Streams",
    y = "Predicted Streams"
  ) +
  theme_minimal()

# First, ensure that you have your predictions with intervals set up properly

# Extract the linear model from caret's train object
lm_model <- cv_model$finalModel

# Generate predictions with both confidence and prediction intervals
pred_conf <- predict(lm_model, newdata = data_complete, interval = "confidence")
pred_pred <- predict(lm_model, newdata = data_complete, interval = "prediction")

# Combine everything into a data frame
plot_data <- data_complete %>%
  mutate(
    predicted_streams = pred_conf[, "fit"],
    conf_low = pred_conf[, "lwr"],
    conf_high = pred_conf[, "upr"],
    pred_low = pred_pred[, "lwr"],
    pred_high = pred_pred[, "upr"]
  ) %>%
  arrange(streams)  # sort by actual streams for smooth ribbons

# Plot with ggplot2
ggplot(plot_data, aes(x = predicted_streams, y = streams)) +
  geom_point(alpha = 0.6, color = "darkblue") +
  geom_abline(intercept = 0, slope = 1, linetype = "dashed", color = "blue") +
  geom_ribbon(aes(ymin = conf_low, ymax = conf_high), fill = "lightblue", alpha = 0.3) +
  geom_ribbon(aes(ymin = pred_low, ymax = pred_high), fill = "orange", alpha = 0.2) +
  labs(
    title = "Predicted vs Actual Streams with Confidence and Prediction Intervals",
    x = "Predicted Streams",
    y = "Actual Streams"
  ) +
  theme_minimal()

# Plot with confidence and prediction intervals as lines (no shaded ribbons)
ggplot(plot_data, aes(x = predicted_streams, y = streams)) +
  geom_point(alpha = 0.6, color = "darkblue") +
  geom_abline(intercept = 0, slope = 1, linetype = "solid", color = "blue") +
  geom_line(aes(y = conf_low), color = "purple", linetype = "dashed") +
  geom_line(aes(y = conf_high), color = "purple", linetype = "dashed") +
  geom_line(aes(y = pred_low), color = "red", linetype = "dotted") +
  geom_line(aes(y = pred_high), color = "red", linetype = "dotted") +
  labs(
    title = "Actual vs Predicted Streams with Confidence and Prediction Interval Lines",
    x = "Predicted Streams",
    y = "Actual Streams"
  ) +
  theme_minimal()

ggplot(plot_data, aes(x = in_spotify_playlists, y = streams)) +
  geom_point() +
  geom_line(aes(y = predicted_streams), color = "blue") +
  geom_ribbon(aes(ymin = conf_low, ymax = conf_high), alpha = 0.2) +
  labs(title = "Model Fit with Confidence Interval",
       y = "Streams", x = "Spotify Playlists") +
  theme_minimal()

# Load required packages
library(shiny)

# Define UI
ui <- fluidPage(
  titlePanel("Predict Spotify Song Streams"),
  tabsetPanel(
    tabPanel("Visualize by Mode",
      sidebarLayout(
        sidebarPanel(
          checkboxGroupInput("selected_modes", "Select Mode(s):",
                             choices = unique(spotify$mode),
                             selected = unique(spotify$mode))
        ),
        mainPanel(
          plotOutput("modePlot")
        )
      )
    ),
    tabPanel("Predict Streams",
      sidebarLayout(
        sidebarPanel(
          numericInput("spotify", "Spotify Playlists:", value = 5000, min = 0),
          numericInput("deezer", "Deezer Playlists:", value = 1000, min = 0),
          numericInput("apple", "Apple Playlists:", value = 2000, min = 0),
          actionButton("predict", "Predict Streams")
        ),
        mainPanel(
          h3("Predicted Streams:"),
          verbatimTextOutput("prediction")
        )
      )
    )
  )
)

# Define server logic
server <- function(input, output) {

  # Reactive prediction
  observeEvent(input$predict, {
    new_input <- data.frame(
      in_spotify_playlists = input$spotify,
      in_deezer_playlists = input$deezer,
      in_apple_playlists = input$apple
    )

    predicted <- predict(final_model, newdata = new_input)

    output$prediction <- renderText({
      format(round(predicted, 0), big.mark = ",")
    })
  })

  output$modePlot <- renderPlot({
    req(input$selected_modes)

    filtered_data <- subset(spotify, mode %in% input$selected_modes)

    ggplot(filtered_data, aes(x = streams, y = in_spotify_playlists, color = mode)) +
      geom_point() +
      labs(title = "Streams vs Playlist Metrics by Mode",
           x = "Streams",
           y = "Number in Spotify Playlists") +
      theme_minimal()
  })
}

# Run the application 
shinyApp(ui = ui, server = server)

G3;
Listening on http://127.0.0.1:7921
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ggplot(plot_data, aes(x = predicted_streams, y = in_spotify_playlists)) +
  geom_point(aes(y = streams), alpha = 0.5) +
  geom_line() +
  geom_ribbon(aes(ymin = conf_low, ymax = conf_high), alpha = 0.2) +
  labs(title = "Prediction with Confidence Intervals")

plot(lm_model)

plot(lm_model, which = 5)